An iris recognition system is an apparatus for performing identification of each individual by differentiating iris patterns of the pupil of an eye, which are unique for each individual. It has superior identification accuracy and excellent security as compared with other biometric method using voice and fingerprints from each individual. A human iris is the region between a pupil and a white sclera of an eye, and iris recognition is a technique for performing identification of each individual based on information that is obtained from an analysis of the iris patterns which are different in each individual.
In general, it is a core technology to efficiently acquire unique characteristic information from input images in the field of an applied technology for performing identification of each individual by utilizing the characteristic information of the human body. A wavelet transform is used to extract characteristics of the iris images, and it is a kind of technique of analyzing signals in multiresolution mode. The wavelet transform is a mathematical theory of formulating a model for signals, systems and a series of processes by using specifically selected signals. These signals are referred to as little waves or wavelets. Recently, the wavelet transform is widely employed in the field of signal and image processing since it has a fast rate as compared with a conventional signal processing algorithm based on the Fourier transform, and it can efficiently accomplish signal localization in time and frequency domains.
On the other hand, the images, which are obtained by extracting only iris patterns from the iris images acquired by image acquisition equipment and normalizing the patterns at a 450×60 size, are used to extract characteristic values through the wavelet transform. Further, a Harr wavelet transform has been widely used in conventional iris recognition, image processing and the like. However, Harr wavelet functions have disadvantages in that the characteristic values are discontinuously and rapidly changed and that high resolution of the images cannot be obtained in a case where the images are again decompressed after they have been compressed. On the contrary, since Daubechies wavelet functions are continuous functions, the disadvantages of the Harr wavelet functions that the values thereof are discontinuously and rapidly changed can be avoided, and the characteristic values can be extracted more accurately and delicately. Therefore, in a case where the images are to be again decompressed after they have been compressed by using the Daubechies wavelet transform, the images can be restored in high resolution nearer to the original images than when the Harr wavelet transform is used. Since the Daubechies wavelet functions are more complicated than the Harr wavelet functions, there is a disadvantage in that more arithmetic quantity may be needed. However, it can be easily overcome by the recent advent of ultrahigh speed microprocessors.
There is also an advantage in that the Daubechies wavelet transform can obtain fine characteristic values in the process of performing the wavelet transform for extracting the characteristic values. That is, if the Daubechies wavelet transform is used, expression of the iris features can be made in a low capacity of data and extraction of the features can be made accurately.
Methods of extracting the characteristic values and forming the characteristic vectors by using Gabor transform been mainly used in the conventional iris recognition field. However, the characteristic vectors generated by these methods are formed to have 256 or more dimensions, and they have at least 256 bytes even though it is assumed that one byte is assigned to one dimension. Thus, there is a problem in that practicability and efficiency can be reduced when it is used in the field where low capacity information is needed. Accordingly, it is necessary to develop a method of forming the low capacity characteristic vectors wherein processing, storage, transfer, search, and the like of the pattern information can be efficiently made. In addition, since a simple method of measuring a distance such as a Hamming distance (HD) between two characteristic vectors (characteristic vectors relevant to the input pattern and stored reference characteristic vectors) is used for pattern classification in a prior art such as U.S. Pat. No. 5,291,560, there are disadvantages in that formation of the reference characteristic vectors through generalization of the pattern information cannot be easily made and information characteristics of each dimension of the characteristic vectors cannot be properly reflected.
That is, in the method of using the Hamming distance in order to verify the two characteristic vectors generated in the form of binary vectors, bit values assigned according to respective dimensions are compared with each other. If they are identical to each other, 0 is given; and if they are different from each other, 1 is given. Then, a value divided by the total number of the dimensions is obtained as a final result. The method is simple and useful in discriminating a degree of similarity between the characteristic vectors consisted of binary codes. When the Hamming distance is used, the comparison result of all the bits becomes 0 if identical data are compared with each other. Thus, the result approaching to 0 implies that the data belong to the persons themselves. If the data do indeed belong to the person, the probability of a degree of similarity will be 0.5. Thus, upon comparison with the other person's data, it is understood that the values converge around 0.5. Accordingly, a proper limit set between 0 and 0.5 will be a boundary for differentiating the data of the persons themselves from the other person's data. The Hamming distance (HD) is excellent in performance thereof in a case where the information is obtained from the extracted iris features by subdividing the data, but it is not suitable when low capacity data is to be used. In other words, in a case where total number of the bits of the characteristic vectors having 256-byte information is 2048, considerably high acceptance rates can be achieved even though the Hamming distance is applied. However, in a case where low capacity characteristic vectors in which the number of characteristic vectors is reduced are used as in the present invention, high acceptance rates cannot be obtained.
On the other hand, in a case where the low capacity characteristic vectors are used, improvement of the acceptance rate is limited to a certain extent since lost information is increased. Thus, a method of preventing loss of the information while maintaining minimum capacity of the characteristic vectors should be considered in the process of generating the characteristic vectors.